<div dir="ltr"><div><div><div><div>Hello,<br><br></div>If you can make the hypothesis
that your data is corrupted by gaussian noise, then you can approximate
the covariance matrix of your estimated parameters. Let p be the vector
of parameters and r(p) the residual vector given by<br>
<br></div>r(p)=sigma^(-1)*(y-Y(p))<br><br></div>where y is your
measuement vector, Y(p) the "simulated" measurement, sigma a diagonal
matrix with the std error for each measurement. If we denote by<br><br></div>
<div>drdp(p) the derivative (or jacobian matrix) of r with respect to p
then the covariance matrice C of parameters can be estimated by<br><br><br></div><div>C=F^(-1)<br><br></div><div>where <br><br>F=drdp(p)' * drdp(p) <br>
<br>is the Fisher information matrix. The diagonal terms of V give you
the variance of the parameters. Of course, even in the gaussian case,
this is a crude approximation.... Here is (attached) a simple example where the derivative is approximated with the "derivative" macro of Scilab.<br><br><br><br></div>S.</div><div class="gmail_extra"><br><br><div class="gmail_quote">
2014-02-19 14:31 GMT+01:00 Yohann <span dir="ltr"><<a href="mailto:yohann.morille@univ-angers.fr" target="_blank">yohann.morille@univ-angers.fr</a>></span>:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
Hi Antoine,<br>
thank you for your answer but<br>
what I need is a confidence interval on each parameter !<br>
Cheers<br>
Yohann<br>
<br>
<br>
<br>
<br>
--<br>
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